Retinal Vessel Segmentation based on Convolutional Neural Network and Connection Domain Detection

We propose a retinal vessel segmentation algorithm based on a convolution neural network and connected domain detection. After defining the discriminant matrix, we constructed and trained a convolutional neural network model, which can realize the mapping relationship from eye fundus grayscale to the discriminant matrix. This model achieves the preliminary segmentation of retinal vessels. The prediction of uncertain pixels is revised by using the geometric characteristics of the vessels and through the analysis of connected regions. The experimental results show good generalization ability, the average segmentation accuracy, specificity, and sensitivity are 96.64%, 97.96%, and 80.68%, respectively. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.